In [116]:
#关闭警告信息
import warnings
warnings.filterwarnings('ignore')
读取数据并对数据进行必要的检查，包括缺失值、数据类型。
In [2]:
#读取数据
import pandas as pd
Data=pd.read_pickle('DNA methylation data/RA DNA methylation/RA_Methylation_Data.pkl')
Data.info()
<class 'pandas.core.frame.DataFrame'> Index: 6425 entries, 7462 to 7002 Columns: 100008 entries, sample_id to DiseaseEncoder dtypes: float64(100002), int64(2), object(4) memory usage: 4.8+ GB
In [3]:
#检查数据空缺值
print(Data.isnull().sum().sum())
136
In [5]:
#处理空缺数据
Data['gender'].fillna('M',inplace=True)
Data['GenderEncoder'].fillna(0,inplace=True)
In [6]:
#检查空缺数据处理结果
print(Data.isnull().sum().sum())
0
In [7]:
#查看数据结构
Data.head(10)
Out[7]:
| sample_id | cg00050873 | cg00212031 | cg00213748 | cg00214611 | cg00455876 | cg01707559 | cg02004872 | cg02011394 | cg02050847 | ... | cg12794168 | cg12799119 | cg12848808 | age | gender | sample_type | disease | GenderEncoder | sample_type_encoder | DiseaseEncoder | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 7462 | train17463 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 1.482415 | -3.938986 | -1.687774 | 39.0 | F | disease tissue | rheumatoid arthritis | 1.0 | 1 | 1 |
| 7463 | train17464 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.890330 | -3.619579 | -1.672671 | 28.0 | F | disease tissue | rheumatoid arthritis | 1.0 | 1 | 1 |
| 7464 | train17465 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 2.049755 | -3.886935 | -2.100192 | 68.0 | F | disease tissue | rheumatoid arthritis | 1.0 | 1 | 1 |
| 7465 | train17466 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.832866 | -4.112908 | -2.324893 | 30.0 | F | disease tissue | rheumatoid arthritis | 1.0 | 1 | 1 |
| 7466 | train17467 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 1.564056 | -3.701353 | -1.814692 | 69.0 | F | disease tissue | rheumatoid arthritis | 1.0 | 1 | 1 |
| 7467 | train17468 | 1.687774 | -4.247583 | 0.0 | -3.744756 | 0.0 | -2.185284 | -3.314031 | 2.570129 | 3.744756 | ... | 1.482415 | -3.580953 | -1.982282 | 40.0 | M | disease tissue | rheumatoid arthritis | 0.0 | 1 | 1 |
| 7468 | train17469 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 1.247949 | -3.886935 | -2.069693 | 47.0 | F | disease tissue | rheumatoid arthritis | 1.0 | 1 | 1 |
| 7469 | train17470 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 1.790011 | -4.051632 | -1.918093 | 53.0 | F | disease tissue | rheumatoid arthritis | 1.0 | 1 | 1 |
| 7470 | train17471 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 1.348848 | -4.051632 | -1.936120 | 62.0 | F | disease tissue | rheumatoid arthritis | 1.0 | 1 | 1 |
| 7471 | train17472 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 1.300641 | -4.322159 | -1.848299 | 49.0 | F | disease tissue | rheumatoid arthritis | 1.0 | 1 | 1 |
10 rows × 100008 columns
使用PCA算法计算保留95%和99%信息的维度，并将数据降维至3维可视化数据分布。
In [9]:
#提取甲基化数据并进行PCA处理
Methylation=Data.iloc[:,1:-7]
print(Methylation.shape)
(6425, 100000)
In [8]:
#执行PCA算法-计算保留99%信息时的维度
from sklearn.decomposition import PCA
Methylaion_PCA=PCA(n_components=0.99)
PCA_Methylation_99=Methylaion_PCA.fit_transform(Methylation) #数据降维
print(PCA_Methylation_99.shape)
(6425, 3981)
In [8]:
#执行PCA算法-计算保留99%信息时的维度
from sklearn.decomposition import PCA
Methylaion_PCA=PCA(n_components=0.95)
PCA_Methylation_95=Methylaion_PCA.fit_transform(Methylation) #数据降维
print(PCA_Methylation_95.shape)
(6425, 2514)
In [9]:
#计算可解释方差比
import numpy as np
import matplotlib.pyplot as plt
Explain_variance=np.cumsum(Methylaion_PCA.explained_variance_ratio_)
#绘图
Explain_variance_plt=plt.figure(dpi=300)
Epl_variance=Explain_variance_plt.add_subplot(111)
Epl_variance.set_title('Explainable variance ratio curve')
Epl_variance.grid(color='black',linestyle='-.',alpha=0.2)
Epl_variance.plot(np.arange(1,len(Explain_variance)+1,1,dtype=int),Explain_variance,color='orange',linestyle='-')
#添加95%可解释方差曲线
Epl_variance.scatter(x=2514,y=0.95,color='green',marker='o')
Epl_variance.axhline(y=0.95, color='green', linestyle='-.')
Epl_variance.axvline(x=2514, color='green', linestyle='-.', label='Retain 95 per cent of explainable variance')
#添加99%可解释方差曲线
Epl_variance.scatter(3981,0.99,color='red',marker='o')
Epl_variance.axhline(y=0.99, color='red', linestyle='-.')
Epl_variance.axvline(x=3981, color='red', linestyle='-.', label='Retain 99 per cent of explainable variance')
Epl_variance.set_xlabel('n_components')
Epl_variance.set_ylabel('explained_variance_ratio')
plt.legend()
plt.show()
特征选择：执行低方差过滤、高相关过滤和Lossa回归进行特征选择。
In [10]:
#对数据采取低方差过滤算法
from pandas import DataFrame
from sklearn.feature_selection import VarianceThreshold
Variance_Selector=VarianceThreshold(threshold=0.5)
VarSele_Data=Variance_Selector.fit_transform(Methylation) #执行低方差过滤
VarSele_Data=DataFrame(VarSele_Data)
VarSele_Data.columns=Variance_Selector.get_feature_names_out()
VarSele_Data.shape
Out[10]:
(6425, 63403)
In [11]:
#构建特征方差数据
VarianseData=DataFrame()
VarianseData['MetaBolite']=Variance_Selector.feature_names_in_
VarianseData['Variances']=Variance_Selector.variances_
VarianseData.shape
Out[11]:
(100000, 2)
In [12]:
#输出为Eecel文件
VarianseData.to_excel('/mnt/workspace/Analysis Data/VarianseData.xlsx','UTF-8')
In [13]:
#执行基于F分布的方差分析进行高相关过滤
from sklearn.feature_selection import SelectKBest, f_classif
Selectk=SelectKBest(score_func=f_classif,k=3981) #保留99%可解释信息
FselectorData=Selectk.fit_transform(VarSele_Data,Data.loc[:,'DiseaseEncoder'])
FselectorData=pd.DataFrame(FselectorData)
feature_nameindex=Selectk.get_support(indices=True) #获取特征索引
feature_names=VarSele_Data.columns #获取特征名
Kfeature_names=[feature_names[i] for i in feature_nameindex]
FselectorData.columns=Kfeature_names
FselectorData.shape
Out[13]:
(6425, 3981)
In [14]:
#获取相关系数信息
KBestF_info=DataFrame()
KBestF_info['Feature']=Kfeature_names #载入特征
KBestF_info['scores']=Selectk.scores_[:3981]
KBestF_info['P value']=Selectk.pvalues_[:3981]
KBestF_info.shape
Out[14]:
(3981, 3)
In [15]:
#输出相关系数数据
KBestF_info.to_excel('/mnt/workspace/Analysis Data/CorrData.xlsx','UTF-8')
In [16]:
#采用Lasso算法进行特征选择
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
Target=Data.loc[:,'DiseaseEncoder']
# 数据标准化
scaler=StandardScaler()
X_scaled=scaler.fit_transform(FselectorData)
# 拆分数据集
X_train,X_test,y_train,y_test=train_test_split(X_scaled,Target, test_size=0.3, random_state=2024)
lasso_model = LogisticRegression(penalty='l1',solver='liblinear',C=0.1,random_state=2024,max_iter=10000) # 设置Lasso模型
lasso_model.fit(X_train,y_train) # 训练模型
Out[16]:
LogisticRegression(C=0.1, max_iter=10000, penalty='l1', random_state=2024,
solver='liblinear')In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
LogisticRegression(C=0.1, max_iter=10000, penalty='l1', random_state=2024,
solver='liblinear')In [17]:
#评估Lasso模型性能
print('Lasso模型训练集Accuracy为:',accuracy_score(y_train,lasso_model.predict(X_train)))
print('Lasso模型测试集Accuracy为:',accuracy_score(y_test,lasso_model.predict(X_test)))
Lasso模型训练集Accuracy为: 0.9968868134311764 Lasso模型测试集Accuracy为: 0.9942946058091287
In [18]:
#查看模型混淆矩阵
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix,ConfusionMatrixDisplay
def Display_ConfusionMatrix(model,data,target):
ConfusionMatrix_result=confusion_matrix(target,model.predict(data),labels=[0,1]) #计算混淆举证
Display_ConfusionMatrix=ConfusionMatrixDisplay(ConfusionMatrix_result,display_labels=['control','rheumatoid arthritis'])
Display_ConfusionMatrix.plot(include_values=True, cmap='viridis', xticks_rotation='horizontal', values_format='d', ax=None)
plt.title('Confusion Matrix')
plt.show()
In [19]:
Display_ConfusionMatrix(lasso_model,X_train,y_train) #训练集
In [20]:
Display_ConfusionMatrix(lasso_model,X_test,y_test) #测试集
In [21]:
#提取特征并进行特征选择
import numpy as np
LassoFeature_Index=np.where(lasso_model.coef_!=0)[1]
LassoFeature=FselectorData.iloc[:,LassoFeature_Index]
LassoFeature.shape
Out[21]:
(6425, 59)
In [22]:
#输出lossa模型
import joblib
joblib.dump(lasso_model,'/mnt/workspace/Analysis Model/LossaModel.pkl')
Out[22]:
['/mnt/workspace/Analysis Model/LossaModel.pkl']
使用Lasso验证DNA甲基化位点特征选择后的结果.
In [23]:
#使用特征选择后的模型验证模型精度-输入[batchs,70],输出[batch,1]
X_TrainEv,X_testEv,y_trainEv,y_testEv=train_test_split(LassoFeature,Target, test_size=0.3, random_state=2024)
lasso_modelEvaluateD=LogisticRegression(penalty='l1',solver='liblinear',C=0.1,random_state=2024,max_iter=10000)
lasso_modelEvaluateD.fit(X_TrainEv,y_trainEv)
Out[23]:
LogisticRegression(C=0.1, max_iter=10000, penalty='l1', random_state=2024,
solver='liblinear')In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
LogisticRegression(C=0.1, max_iter=10000, penalty='l1', random_state=2024,
solver='liblinear')In [24]:
#使用特征选择后的数据集再次训练模型评估特征选择效果-10折交叉验证
from sklearn.metrics import accuracy_score
from sklearn.model_selection import KFold,cross_val_score
from sklearn.model_selection import cross_validate
def Model_Evelate_CV(Model,Data,Target):
cv=KFold(n_splits=10, shuffle=True, random_state=2025)
kv_scores =cross_validate(Model,Data,Target,cv=cv,scoring='accuracy',
return_train_score=True)
print('Fit_time:',kv_scores['fit_time'])
print('Mean Fit_time:',kv_scores['fit_time'].mean())
print('score_time:',kv_scores['score_time'])
print('Mean score_time:',kv_scores['score_time'].mean())
print('train_score:',kv_scores['train_score'])
print('Mean train_score:',kv_scores['train_score'].mean())
print('test_score:',kv_scores['test_score'])
print('Mean test_score:',kv_scores['test_score'].mean())
#用特征选择后的数据集再次训练模型评估特征选择效果-10折交叉验证
lasso_modelEvaluate=LogisticRegression(penalty='l1',solver='liblinear',C=0.1,random_state=2024,max_iter=10000) # 设置Lasso模型
Model_Evelate_CV(Model=lasso_modelEvaluate,Data=LassoFeature,Target=Target)
Fit_time: [0.13763237 0.15320015 0.17018366 0.14581394 0.12758136 0.13371754 0.1441164 0.13194776 0.13309407 0.13670778] Mean Fit_time: 0.14139950275421143 score_time: [0.00301838 0.00292587 0.00316906 0.00275707 0.003124 0.00312471 0.00284743 0.00251055 0.0025866 0.00309825] Mean score_time: 0.0029161930084228515 train_score: [0.99688689 0.99740574 0.99723279 0.99723279 0.99723279 0.99740619 0.99740619 0.99706035 0.99671451 0.99757911] Mean train_score: 0.9972157356217635 test_score: [0.99377916 0.99377916 0.99533437 0.99688958 0.99688958 0.99221184 0.99376947 0.99688474 0.99844237 0.9953271 ] Mean test_score: 0.9953307364718537
In [25]:
#训练集混淆举证
Display_ConfusionMatrix(lasso_modelEvaluateD,X_TrainEv,y_trainEv)
In [26]:
#测试集混淆举证
Display_ConfusionMatrix(lasso_modelEvaluateD,X_testEv,y_testEv)
In [27]:
#整合并输出特征选择结果
CG_Feature=Data.iloc[:,LassoFeature_Index]
DNA_Methylstion_Feature=pd.concat([CG_Feature,Data.iloc[:,-7:]],axis=1)
DNA_Methylstion_Feature.shape
Out[27]:
(6425, 66)
In [28]:
#输出特征选择甲基化数据
DNA_Methylstion_Feature.to_pickle('/mnt/workspace/DNA methylation data/RA DNA methylation/RA_Methylation_Feature.pkl')
DNA_Methylstion_Feature.to_excel('/mnt/workspace/DNA methylation data/RA DNA methylation/RA_Methylation_Feature.xlsx','UTF-8')
DNA_Methylstion_Feature.to_csv('/mnt/workspace/DNA methylation data/RA DNA methylation/RA_Methylation_Feature.csv')
使用机器学习提取关键甲基化位点
In [1]:
#读取特征选择后的甲基化数据
import pandas as pd
MethylationFeature=pd.read_csv('/mnt/workspace/RA_Methylation_Feature.csv')
MethylationFeature.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 6425 entries, 0 to 6424 Data columns (total 67 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Unnamed: 0 6425 non-null int64 1 cg00455876 6425 non-null float64 2 cg05544622 6425 non-null float64 3 cg00423014 6425 non-null float64 4 cg00478198 6425 non-null float64 5 cg00776430 6425 non-null float64 6 cg01938887 6425 non-null float64 7 cg02714462 6425 non-null float64 8 cg02896361 6425 non-null float64 9 cg02971902 6425 non-null float64 10 cg03601619 6425 non-null float64 11 cg04029664 6425 non-null float64 12 cg04302300 6425 non-null float64 13 cg04699313 6425 non-null float64 14 cg05257372 6425 non-null float64 15 cg05443523 6425 non-null float64 16 cg06411441 6425 non-null float64 17 cg07066594 6425 non-null float64 18 cg08141049 6425 non-null float64 19 cg08311689 6425 non-null float64 20 cg09357232 6425 non-null float64 21 cg10315562 6425 non-null float64 22 cg11704979 6425 non-null float64 23 cg11923788 6425 non-null float64 24 cg12944030 6425 non-null float64 25 cg14061423 6425 non-null float64 26 cg14745925 6425 non-null float64 27 cg15388264 6425 non-null float64 28 cg16078210 6425 non-null float64 29 cg17482649 6425 non-null float64 30 cg17488844 6425 non-null float64 31 cg17794813 6425 non-null float64 32 cg19248041 6425 non-null float64 33 cg19532714 6425 non-null float64 34 cg19603571 6425 non-null float64 35 cg19784262 6425 non-null float64 36 cg22055524 6425 non-null float64 37 cg22219869 6425 non-null float64 38 cg22221554 6425 non-null float64 39 cg22561883 6425 non-null float64 40 cg23299576 6425 non-null float64 41 cg23925558 6425 non-null float64 42 cg23993836 6425 non-null float64 43 cg24627619 6425 non-null float64 44 cg26012731 6425 non-null float64 45 cg26039926 6425 non-null float64 46 cg26936230 6425 non-null float64 47 cg00097228 6425 non-null float64 48 cg00152515 6425 non-null float64 49 cg00306390 6425 non-null float64 50 cg00342358 6425 non-null float64 51 cg00397635 6425 non-null float64 52 cg00455424 6425 non-null float64 53 cg00534295 6425 non-null float64 54 cg00543485 6425 non-null float64 55 cg00581848 6425 non-null float64 56 cg01393939 6425 non-null float64 57 cg01439876 6425 non-null float64 58 cg01446477 6425 non-null float64 59 cg01515508 6425 non-null float64 60 age 6425 non-null float64 61 gender 6425 non-null object 62 sample_type 6425 non-null object 63 disease 6425 non-null object 64 GenderEncoder 6425 non-null float64 65 sample_type_encoder 6425 non-null int64 66 DiseaseEncoder 6425 non-null int64 dtypes: float64(61), int64(3), object(3) memory usage: 3.3+ MB
In [2]:
MethylationFeature.isnull().sum().sum() #检测空缺数据
Out[2]:
0
In [3]:
#统计性别数据
from collections import Counter
print(Counter(MethylationFeature['gender']))
MethylationFeature['gender'].hist()
Counter({'F': 3430, 'M': 2995})
Out[3]:
<Axes: >
In [4]:
#统计疾病数据
print(Counter(MethylationFeature['disease']))
MethylationFeature['disease'].hist()
Counter({'control': 6266, 'rheumatoid arthritis': 159})
Out[4]:
<Axes: >
进行机器学习分析，筛选类风湿性关节炎特征甲基化位点。
In [5]:
Methylation=MethylationFeature.iloc[:,1:-7] #甲基化数据
MapData=MethylationFeature.loc[:,['age','GenderEncoder','DiseaseEncoder']] #其他附加数据
MLData=pd.concat([Methylation,MapData],axis=1) #数据合并
MLData.shape
Out[5]:
(6425, 62)
正常样本与疾病样本总量差异较大，使用数据重采样算法解决数据类别分布不均衡问题。
In [6]:
#查看模型混淆矩阵
from sklearn.metrics import confusion_matrix,ConfusionMatrixDisplay
def Display_ConfusionMatrix(model,data,target):
ConfusionMatrix_result=confusion_matrix(target,model.predict(data),labels=[0,1]) #计算混淆举证
Display_ConfusionMatrix=ConfusionMatrixDisplay(ConfusionMatrix_result,display_labels=['control','rheumatoid arthritis'])
Display_ConfusionMatrix.plot(include_values=True, cmap='viridis', xticks_rotation='horizontal', values_format='d', ax=None)
plt.title('Confusion Matrix')
plt.show()
In [7]:
#计算测试集PR曲线
from pandas import DataFrame
from sklearn.metrics import precision_recall_curve,accuracy_score
def PR_Curve(Model,Data,Label):
X_train,X_test,y_train,y_test=train_test_split(Data,Label,train_size=0.7,random_state=2024) #划分数据集
predict_score=Model.predict_proba(X_test)[:, 1] #获取概率值
predict=Model.predict(X_test) #获取预测标签
accuracy=accuracy_score(y_test,predict)
precision, recall, thresholds = precision_recall_curve(y_test, predict_score) #计算PR曲线
PR=DataFrame() #将PR曲线数据合并到DataFrame
#PR['thresholds']=thresholds
PR['recall']=recall
PR['precision']=precision
return PR,accuracy
In [8]:
#计算测试集ROC曲线
from sklearn.metrics import roc_curve, auc
def ROC_Curve(Model,Data,Label):
X_train,X_test,y_train,y_test=train_test_split(Data,Label,train_size=0.7,random_state=2024) #划分数据集
predict_score=Model.predict_proba(X_test)[:, 1] #获取概率值
fpr, tpr, thresholds = roc_curve(y_test, predict_score)
roc_auc = auc(fpr, tpr) #计算AUC
ROC=DataFrame() #将PR曲线数据合并到DataFrame
#ROC['thresholds']=thresholds
ROC['tpr']=tpr
ROC['fpr']=fpr
return ROC,roc_auc
In [9]:
#编写模型训练评估函数
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.metrics import precision_score,recall_score,f1_score,accuracy_score
def RunTestModel(Model,Data,Label):
X_train,X_test,y_train,y_test=train_test_split(Data,Label,train_size=0.7,random_state=2024) #划分数据集
Model.fit(X_train,y_train) #训练模型
TrainPredict=Model.predict(X_train) #计算训练集指标
TrainPrecision=precision_score(y_train,TrainPredict)
TrainRecall=recall_score(y_train,TrainPredict)
TrainF1=f1_score(y_train,TrainPredict)
TrainAcuracy=accuracy_score(y_train,TrainPredict)
print('模型训练集Precision:{0},Recall:{1},F1_Score:{2},Accuracy:{3}'.format(TrainPrecision,TrainRecall,TrainF1,TrainAcuracy))
TestPredict=Model.predict(X_test) #测试集预测结果
TestPrecision=precision_score(y_test,TestPredict)
TestRecall=recall_score(y_test,TestPredict)
TestF1=f1_score(y_test,TestPredict)
TestAccuracy=accuracy_score(y_test,TestPredict)
print('模型测试集Precision:{0},Recall:{1},F1_score:{2},Accuracy:{3}'.format(TestPrecision,TestRecall,TestF1,TestAccuracy))
print('-------------------测试集混淆举证-------------------')
Display_ConfusionMatrix(model=Model,data=X_test,target=y_test)
In [10]:
#切割数据
MLTestData=MLData.iloc[:,:-1]
MLTestLabel=MLData.iloc[:,-1]
In [11]:
#数据归一化
from pandas import DataFrame
from sklearn.preprocessing import StandardScaler,MinMaxScaler
ColumnsNames=MLTestData.columns
Stand=StandardScaler()
StandData=Stand.fit_transform(MLTestData)
MinMax=MinMaxScaler(feature_range=(0,1))
MinMaxData=MinMax.fit_transform(StandData)
MLTestData=DataFrame(MinMaxData)
MLTestData.columns=ColumnsNames
In [12]:
#使用随机森林算法作为衡量基准测试数据重采样算法性能
from sklearn.ensemble import RandomForestClassifier
Forest=RandomForestClassifier(random_state=2025)
RunTestModel(Model=Forest,Data=MLTestData,Label=MLTestLabel)
模型训练集Precision:1.0,Recall:1.0,F1_Score:1.0,Accuracy:1.0 模型测试集Precision:0.75,Recall:0.07142857142857142,F1_score:0.13043478260869565,Accuracy:0.979253112033195 -------------------测试集混淆举证-------------------
In [14]:
RF_PR,RF_Accuracy=PR_Curve(Model=Forest,Data=MLTestData,Label=MLTestLabel)
In [15]:
RF_ROC,RF_AUC=ROC_Curve(Model=Forest,Data=MLTestData,Label=MLTestLabel)
In [19]:
#输出相关数据
RF_PR.to_excel('/mnt/workspace/Analysis Data/Resample PR Data/RF_PR.xlsx')
RF_ROC.to_excel('/mnt/workspace/Analysis Data/Resample ROC Data/RF_ROC.xlsx')
测试欠采样算法。
In [22]:
#使用CNN算法进行欠采样
from imblearn.under_sampling import CondensedNearestNeighbour
CNN=CondensedNearestNeighbour(sampling_strategy='not minority',random_state=2024,n_jobs=-1)
CNN_TestData,CNN_TestLabel=CNN.fit_resample(MLTestData,MLTestLabel)
Counter(CNN_TestLabel)
Out[22]:
Counter({0: 338, 1: 159})
In [23]:
ForestCNN=RandomForestClassifier(random_state=2025)
RunTestModel(Model=ForestCNN,Data=CNN_TestData,Label=CNN_TestLabel)
模型训练集Precision:1.0,Recall:1.0,F1_Score:1.0,Accuracy:1.0 模型测试集Precision:0.675,Recall:0.6136363636363636,F1_score:0.6428571428571429,Accuracy:0.8 -------------------测试集混淆举证-------------------
In [24]:
CNN_RF_PR,CNN_RF_Accuracy=PR_Curve(Model=ForestCNN,Data=CNN_TestData,Label=CNN_TestLabel)
CNN_RF_ROC,CNN_RF_AUC=ROC_Curve(Model=ForestCNN,Data=CNN_TestData,Label=CNN_TestLabel)
In [25]:
#输出相关数据
CNN_RF_PR.to_excel('/mnt/workspace/Analysis Data/Resample PR Data/CNN_RF_PR.xlsx')
CNN_RF_ROC.to_excel('/mnt/workspace/Analysis Data/Resample ROC Data/CNN_RF_ROC.xlsx')
In [26]:
#使用IHT算法进行欠采样
from imblearn.under_sampling import InstanceHardnessThreshold
IHT=InstanceHardnessThreshold(random_state=2024,cv=5,n_jobs=-1)
IHT_TestData,IHT_TestLabel=IHT.fit_resample(MLTestData,MLTestLabel)
Counter(IHT_TestLabel)
Out[26]:
Counter({0: 3767, 1: 159})
In [27]:
ForestIHT=RandomForestClassifier(random_state=2025)
RunTestModel(Model=ForestIHT,Data=IHT_TestData,Label=IHT_TestLabel)
模型训练集Precision:1.0,Recall:1.0,F1_Score:1.0,Accuracy:1.0 模型测试集Precision:1.0,Recall:0.7719298245614035,F1_score:0.8712871287128713,Accuracy:0.9889643463497453 -------------------测试集混淆举证-------------------
In [28]:
IHT_RF_PR,IHT_RF_Accuracy=PR_Curve(Model=ForestIHT,Data=IHT_TestData,Label=IHT_TestLabel)
IHT_RF_ROC,IHT_RF_AUC=ROC_Curve(Model=ForestIHT,Data=IHT_TestData,Label=IHT_TestLabel)
#输出相关数据
IHT_RF_PR.to_excel('/mnt/workspace/Analysis Data/Resample PR Data/IHT_RF_PR.xlsx')
IHT_RF_ROC.to_excel('/mnt/workspace/Analysis Data/Resample ROC Data/IHT_RF_ROC.xlsx')
In [29]:
#使用NearMiss进行欠采样
from imblearn.under_sampling import NearMiss
NM=NearMiss(sampling_strategy='not minority',n_jobs=-1)
NM_TestData,NM_TestLabel=NM.fit_resample(MLTestData,MLTestLabel)
Counter(NM_TestLabel)
Out[29]:
Counter({0: 159, 1: 159})
In [30]:
ForestNM=RandomForestClassifier(random_state=2025)
RunTestModel(Model=ForestNM,Data=NM_TestData,Label=NM_TestLabel)
模型训练集Precision:1.0,Recall:1.0,F1_Score:1.0,Accuracy:1.0 模型测试集Precision:0.8653846153846154,Recall:0.8490566037735849,F1_score:0.8571428571428571,Accuracy:0.84375 -------------------测试集混淆举证-------------------
In [31]:
NM_RF_PR,NM_RF_Accuracy=PR_Curve(Model=ForestNM,Data=NM_TestData,Label=NM_TestLabel)
NM_RF_ROC,NM_RF_AUC=ROC_Curve(Model=ForestNM,Data=NM_TestData,Label=NM_TestLabel)
#输出相关数据
NM_RF_PR.to_excel('/mnt/workspace/Analysis Data/Resample PR Data/NM_RF_PR.xlsx')
NM_RF_ROC.to_excel('/mnt/workspace/Analysis Data/Resample ROC Data/NM_RF_ROC.xlsx')
In [32]:
#使用NeighbourhoodCleaningRule进行欠采样
from imblearn.under_sampling import NeighbourhoodCleaningRule
NBC=NeighbourhoodCleaningRule(sampling_strategy='not minority',n_jobs=-1)
NBC_TestData,NBC_TestLabel=NBC.fit_resample(MLTestData,MLTestLabel)
Counter(NBC_TestLabel)
Out[32]:
Counter({0: 6109, 1: 159})
In [33]:
ForestNBC=RandomForestClassifier(random_state=2025)
RunTestModel(Model=ForestNBC,Data=NBC_TestData,Label=NBC_TestLabel)
模型训练集Precision:1.0,Recall:1.0,F1_Score:1.0,Accuracy:1.0 模型测试集Precision:1.0,Recall:0.24390243902439024,F1_score:0.39215686274509803,Accuracy:0.9835194045720361 -------------------测试集混淆举证-------------------
In [34]:
NBC_RF_PR,NBC_RF_Accuracy=PR_Curve(Model=ForestNBC,Data=NBC_TestData,Label=NBC_TestLabel)
NBC_RF_ROC,NBC_RF_AUC=ROC_Curve(Model=ForestNBC,Data=NBC_TestData,Label=NBC_TestLabel)
#输出相关数据
NBC_RF_PR.to_excel('/mnt/workspace/Analysis Data/Resample PR Data/NBC_RF_PR.xlsx')
NBC_RF_ROC.to_excel('/mnt/workspace/Analysis Data/Resample ROC Data/NBC_RF_ROC.xlsx')
In [35]:
#使用OneSidedSelection进行欠采样
from imblearn.under_sampling import OneSidedSelection
OSS=OneSidedSelection(sampling_strategy='not minority',random_state=2024,n_jobs=-1)
OSS_TestData,OSS_TestLabel=OSS.fit_resample(MLTestData,MLTestLabel)
Counter(OSS_TestLabel)
Out[35]:
Counter({0: 6163, 1: 159})
In [36]:
ForestOSS=RandomForestClassifier(random_state=2025)
RunTestModel(Model=ForestOSS,Data=OSS_TestData,Label=OSS_TestLabel)
模型训练集Precision:1.0,Recall:1.0,F1_Score:1.0,Accuracy:1.0 模型测试集Precision:1.0,Recall:0.10869565217391304,F1_score:0.19607843137254902,Accuracy:0.9783869267264101 -------------------测试集混淆举证-------------------
In [37]:
OSS_RF_PR,OSS_RF_Accuracy=PR_Curve(Model=ForestOSS,Data=OSS_TestData,Label=OSS_TestLabel)
OSS_RF_ROC,OSS_RF_AUC=ROC_Curve(Model=ForestOSS,Data=OSS_TestData,Label=OSS_TestLabel)
#输出相关数据
OSS_RF_PR.to_excel('/mnt/workspace/Analysis Data/Resample PR Data/OSS_RF_PR.xlsx')
OSS_RF_ROC.to_excel('/mnt/workspace/Analysis Data/Resample ROC Data/OSS_RF_ROC.xlsx')
In [38]:
#使用随机欠采样进行数据欠采样
from imblearn.under_sampling import RandomUnderSampler
RUS=RandomUnderSampler(sampling_strategy='not minority',random_state=2024)
RUS_TestData,RUS_TestLabel=RUS.fit_resample(MLTestData,MLTestLabel)
Counter(RUS_TestLabel)
Out[38]:
Counter({0: 159, 1: 159})
In [39]:
ForestRUS=RandomForestClassifier(random_state=2025)
RunTestModel(Model=ForestRUS,Data=RUS_TestData,Label=RUS_TestLabel)
模型训练集Precision:1.0,Recall:1.0,F1_Score:1.0,Accuracy:1.0 模型测试集Precision:0.9444444444444444,Recall:0.9622641509433962,F1_score:0.9532710280373832,Accuracy:0.9479166666666666 -------------------测试集混淆举证-------------------
In [40]:
RUS_RF_PR,RUS_RF_Accuracy=PR_Curve(Model=ForestRUS,Data=RUS_TestData,Label=RUS_TestLabel)
RUS_RF_ROC,RUS_RF_AUC=ROC_Curve(Model=ForestRUS,Data=RUS_TestData,Label=RUS_TestLabel)
#输出相关数据
RUS_RF_PR.to_excel('/mnt/workspace/Analysis Data/Resample PR Data/RUS_RF_PR.xlsx')
RUS_RF_ROC.to_excel('/mnt/workspace/Analysis Data/Resample ROC Data/RUS_RF_ROC.xlsx')
In [41]:
#绘制PR曲线
import matplotlib.pyplot as plt
PR_curve=plt.figure(dpi=300)
PR_ax=PR_curve.add_subplot(111)
PR_ax.set_title('Precision-Recall curve')
PR_ax.plot(RF_PR['recall'], RF_PR['precision'],color='red',label='RandomForest=%f'%RF_Accuracy)
PR_ax.plot(CNN_RF_PR['recall'], CNN_RF_PR['precision'],color='green',label='CNN=%f'%CNN_RF_Accuracy)
PR_ax.plot(IHT_RF_PR['recall'], IHT_RF_PR['precision'],color='blue',label='IHT=%f'%IHT_RF_Accuracy)
PR_ax.plot(NBC_RF_PR['recall'], NBC_RF_PR['precision'],color='yellow',label='NBC=%f'%NBC_RF_Accuracy)
PR_ax.plot(NM_RF_PR['recall'], NM_RF_PR['precision'],color='purple',label='NM=%f'%NM_RF_Accuracy)
PR_ax.plot(OSS_RF_PR['recall'], OSS_RF_PR['precision'],color='cyan',label='OSS=%f'%OSS_RF_Accuracy)
PR_ax.plot(RUS_RF_PR['recall'], RUS_RF_PR['precision'],color='pink',label='RUS=%f'%RUS_RF_Accuracy)
PR_ax.plot([0,1],[1,0],linestyle='-.',color='black')
PR_ax.set_xlabel('Recall')
PR_ax.set_ylabel('Precision')
plt.legend(loc="best")
plt.show()
In [42]:
#绘制ROC曲线
import matplotlib.pyplot as plt
ROC_curve=plt.figure(dpi=300)
ROC_ax=ROC_curve.add_subplot(111)
ROC_ax.set_title('ROC Curve')
ROC_ax.plot(RF_ROC['fpr'], RF_ROC['tpr'],color='red',label='RandomForest=%f'%RF_AUC)
ROC_ax.plot(CNN_RF_ROC['fpr'], CNN_RF_ROC['tpr'],color='green',label='CNN=%f'%CNN_RF_AUC)
ROC_ax.plot(IHT_RF_ROC['fpr'], IHT_RF_ROC['tpr'],color='blue',label='IHT=%f'%IHT_RF_AUC)
ROC_ax.plot(NBC_RF_ROC['fpr'], NBC_RF_ROC['tpr'],color='yellow',label='NBC=%f'%NBC_RF_AUC)
ROC_ax.plot(NM_RF_ROC['fpr'], NM_RF_ROC['tpr'],color='purple',label='NM=%f'%NM_RF_AUC)
ROC_ax.plot(OSS_RF_ROC['fpr'], OSS_RF_ROC['tpr'],color='cyan',label='OSS=%f'%OSS_RF_AUC)
ROC_ax.plot(RUS_RF_ROC['fpr'], RUS_RF_ROC['tpr'],color='pink',label='RUS=%f'%RUS_RF_AUC)
ROC_ax.plot([0,1],[0,1],linestyle='-.',color='black')
ROC_ax.set_xlim([-0.05, 1.0])
ROC_ax.set_ylim([0, 1.05])
ROC_ax.set_xlabel('FPR')
ROC_ax.set_ylabel('TPR')
plt.legend(loc="best")
plt.show()
In [122]:
import joblib
Model=[Forest,ForestCNN,ForestIHT,ForestNM,ForestNBC,ForestOSS,ForestRUS]
ModelStr=['Forest.pkl','ForestCNN.pkl','ForestIHT.pkl','ForestNM.pkl','ForestNBC.pkl','ForestOSS.pkl','ForestRUS.pkl']
try:
for model,modelstr in zip(Model,ModelStr):
joblib.dump(model,'/mnt/workspace/Analysis Model/Resample Models/'+modelstr)
print('模型保存成功！')
except:
print('模型保存异常！！！')
模型保存成功！
经过混淆矩阵、PR曲线、Roc曲线综合评估，选择随机过采样算法进行数据重采样。数据重采样后进行机器学习和深度神经网络训练并采用可解释性机器学习框架提取关键甲基化位点进行特征分析。
In [123]:
MethylationRUS=MethylationFeature.iloc[:,1:-1]
LabelRUS=MethylationFeature.iloc[:,-1]
In [124]:
#合并过采样结果
RUS=RandomUnderSampler(sampling_strategy='not minority',random_state=2024)
RUS_Methylation,RUS_Label=RUS.fit_resample(MethylationRUS,LabelRUS)
Counter(RUS_Label)
Out[124]:
Counter({0: 159, 1: 159})
In [125]:
RUSData=pd.concat([RUS_Methylation,RUS_Label],axis=1)
RUSData.shape
Out[125]:
(318, 66)
In [ ]:
#输出计算合并结果
RUSData.to_csv('/mnt/workspace/DNA methylation data/RA DNA methylation/RUS DNA Methylation Data.csv')
RUSData.to_excel('/mnt/workspace/DNA methylation data/RA DNA methylation/RUS DNA Methylation Data.xlsx','UFT-8')
读取数据进行机器学习提取特征位点。
In [1]:
#读取数据
import pandas as pd
MethylationData=pd.read_csv('/mnt/workspace/DNA methylation data/RA DNA methylation/RUS DNA Methylation Data.csv')
MethylationData.shape
Out[1]:
(318, 67)
In [2]:
#检查空缺数据
MethylationData.isnull().sum().sum()
Out[2]:
0
In [3]:
#数据选择和缩放数据
MLFeatureData=MethylationData.iloc[:,1:-6]
MLFeatureData=pd.concat([MLFeatureData,MethylationData.loc[:,'GenderEncoder']],axis=1)
MLFeatureData
Out[3]:
| cg00455876 | cg05544622 | cg00423014 | cg00478198 | cg00776430 | cg01938887 | cg02714462 | cg02896361 | cg02971902 | cg03601619 | ... | cg00455424 | cg00534295 | cg00543485 | cg00581848 | cg01393939 | cg01439876 | cg01446477 | cg01515508 | age | GenderEncoder | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.000000 | 0.000000 | 1.202370 | 0.224898 | -1.672671 | -1.271175 | 3.201956 | -0.480968 | -0.739857 | 3.406479 | ... | -4.940737 | -3.343970 | -4.178048 | -2.616463 | 3.149987 | -2.207487 | -3.100385 | 3.701353 | 19.0000 | 1.0 |
| 1 | 0.000000 | 0.000000 | 1.019899 | 0.409549 | -1.578093 | -1.130596 | 3.744756 | -0.289957 | -0.180452 | 2.555085 | ... | -3.790034 | -6.163916 | -6.811545 | -2.468664 | 0.152263 | -2.207487 | -3.406479 | 3.543689 | 12.0000 | 1.0 |
| 2 | 0.000000 | 0.000000 | 0.241113 | 0.140201 | -0.663152 | -1.168907 | 3.507691 | -0.076021 | -0.570906 | 3.029956 | ... | -2.196336 | -3.938986 | -4.247583 | 0.000000 | 0.405382 | -2.414368 | -4.051632 | 1.557086 | 71.8192 | 1.0 |
| 3 | 0.510719 | 4.178048 | 1.839820 | -1.848299 | -2.698069 | -2.921730 | 4.807960 | -3.701353 | -3.938986 | 2.401172 | ... | -3.790034 | -4.489850 | -6.163916 | -2.732410 | 3.124904 | -2.337277 | -3.228904 | 4.178048 | 23.0000 | 0.0 |
| 4 | 0.000000 | 0.000000 | 0.837604 | 0.351505 | -0.809306 | -1.456115 | 4.402578 | 0.000000 | -0.772045 | 2.681278 | ... | -3.580953 | -3.256540 | -5.773449 | 0.000000 | 2.241551 | -2.414368 | -3.659672 | 3.938986 | 84.0000 | 1.0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 313 | 0.000000 | 0.000000 | 0.000000 | 0.072017 | 0.000000 | -1.798190 | 3.374769 | 0.000000 | -0.654254 | 2.300497 | ... | -3.744756 | -4.585271 | -3.886935 | 0.000000 | 2.441207 | -2.496772 | -4.051632 | 4.402578 | 34.0000 | 1.0 |
| 314 | 0.000000 | 3.659672 | 0.000000 | -2.600812 | -3.149987 | -4.112908 | 4.585271 | 0.000000 | -4.489850 | 3.201956 | ... | -3.790034 | -4.178048 | -5.273603 | 0.000000 | 2.881228 | -2.253116 | -3.284902 | 3.993781 | 51.0000 | 0.0 |
| 315 | 0.000000 | 0.000000 | 1.130596 | 0.039997 | 0.000000 | -2.163468 | 3.007447 | 0.000000 | -0.767424 | 2.020410 | ... | -4.112908 | -4.051632 | -6.163916 | 0.000000 | 0.000000 | -2.570129 | -3.701353 | 3.472874 | 45.0000 | 1.0 |
| 316 | 0.000000 | 0.000000 | 0.000000 | 0.140201 | 0.000000 | -1.578093 | 3.149987 | 0.000000 | -0.506456 | 2.555085 | ... | -3.343970 | -3.790034 | -4.112908 | 0.000000 | 2.785946 | -2.616463 | -3.543689 | 4.178048 | 31.0000 | 1.0 |
| 317 | 0.000000 | 0.000000 | 1.672671 | -0.027996 | -1.082414 | -1.141464 | 2.648415 | 0.000000 | -0.426255 | 2.632328 | ... | -3.659672 | -4.807960 | -5.273603 | 0.000000 | 2.349789 | -2.349789 | -3.993781 | 4.940737 | 58.0000 | 1.0 |
318 rows × 61 columns
In [4]:
#数据缩放和标准化
from pandas import DataFrame
from sklearn.preprocessing import StandardScaler,MinMaxScaler
FeatureNames=MLFeatureData.columns #保存变量名
Standard=StandardScaler() #标准化数据
StandardData=Standard.fit_transform(MLFeatureData)
MinMax=MinMaxScaler(feature_range=(0,1)) #缩放数据
MinMaxData=MinMax.fit_transform(StandardData)
MLRunData=DataFrame(MinMaxData)
MLRunData.columns=FeatureNames
MLRunData.shape
Out[4]:
(318, 61)
In [5]:
#获取标签数据
ReMLLabel=MethylationData.iloc[:,-1]
ReMLLabel
Out[5]:
0 0
1 0
2 0
3 0
4 0
..
313 1
314 1
315 1
316 1
317 1
Name: DiseaseEncoder, Length: 318, dtype: int64
In [6]:
#查看模型混淆矩阵
from sklearn.metrics import confusion_matrix,ConfusionMatrixDisplay
def Display_ConfusionMatrix(model,data,target):
ConfusionMatrix_result=confusion_matrix(target,model.predict(data),labels=[0,1]) #计算混淆举证
Display_ConfusionMatrix=ConfusionMatrixDisplay(ConfusionMatrix_result,display_labels=['control','rheumatoid arthritis'])
Display_ConfusionMatrix.plot(include_values=True, cmap='viridis', xticks_rotation='horizontal', values_format='d', ax=None)
plt.title('Confusion Matrix')
plt.show()
In [7]:
#计算PR曲线
from pandas import DataFrame
from sklearn.metrics import precision_recall_curve,accuracy_score
def PR_Curve(Model,Data,Label):
X_train,X_test,y_train,y_test=train_test_split(Data,Label,train_size=0.7,random_state=2025) #划分数据集
predict_score=Model.predict_proba(X_test)[:, 1] #获取概率值
predict=Model.predict(X_test) #获取预测标签
accuracy=accuracy_score(y_test,predict)
precision, recall, thresholds = precision_recall_curve(y_test, predict_score) #计算PR曲线
PR=DataFrame() #将PR曲线数据合并到DataFrame
#PR['thresholds']=thresholds
PR['recall']=recall
PR['precision']=precision
return PR,accuracy
In [8]:
#计算ROC曲线
from sklearn.metrics import roc_curve, auc
def ROC_Curve(Model,Data,Label):
X_train,X_test,y_train,y_test=train_test_split(Data,Label,train_size=0.7,random_state=2025) #划分数据集
predict_score=Model.predict_proba(X_test)[:, 1] #获取概率值
fpr, tpr, thresholds = roc_curve(y_test, predict_score)
roc_auc = auc(fpr, tpr) #计算AUC
ROC=DataFrame() #将PR曲线数据合并到DataFrame
#ROC['thresholds']=thresholds
ROC['tpr']=tpr
ROC['fpr']=fpr
return ROC,roc_auc
In [9]:
#模型训练评估函数
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.metrics import precision_score,recall_score,f1_score,accuracy_score
def RunMLModel(Model,Data,Label):
X_train,X_test,y_train,y_test=train_test_split(Data,Label,train_size=0.7,random_state=2025) #划分数据集
Model.fit(X_train,y_train) #训练模型
TrainPredict=Model.predict(X_train) #计算训练集指标
TrainPrecision=precision_score(y_train,TrainPredict)
TrainRecall=recall_score(y_train,TrainPredict)
TrainF1=f1_score(y_train,TrainPredict)
TrainAcuracy=accuracy_score(y_train,TrainPredict)
print('模型训练集Precision:{0},Recall:{1},F1_Score:{2},Accuracy:{3}'.format(TrainPrecision,TrainRecall,TrainF1,TrainAcuracy))
TestPredict=Model.predict(X_test) #测试集预测结果
TestPrecision=precision_score(y_test,TestPredict)
TestRecall=recall_score(y_test,TestPredict)
TestF1=f1_score(y_test,TestPredict)
TestAccuracy=accuracy_score(y_test,TestPredict)
print('模型测试集Precision:{0},Recall:{1},F1_score:{2},Accuracy:{3}'.format(TestPrecision,TestRecall,TestF1,TestAccuracy))
print('-------------------测试集混淆举证-------------------')
Display_ConfusionMatrix(model=Model,data=X_test,target=y_test)
In [10]:
#测试Logistic回归
from sklearn.linear_model import LogisticRegression
Logistic=LogisticRegression(penalty="l2",dual=True,tol=1e-4,C=1.0,fit_intercept=True,random_state=2024,solver='liblinear',
max_iter=100,multi_class="auto",verbose=0, warm_start=False,n_jobs=-1,l1_ratio=None)
RunMLModel(Model=Logistic,Data=MLRunData,Label=ReMLLabel)
/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_logistic.py:1247: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. From then on, it will always use 'multinomial'. Leave it to its default value to avoid this warning. warnings.warn( /usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_logistic.py:1271: UserWarning: 'n_jobs' > 1 does not have any effect when 'solver' is set to 'liblinear'. Got 'n_jobs' = 8. warnings.warn(
模型训练集Precision:0.8870967741935484,Recall:0.9649122807017544,F1_Score:0.9243697478991597,Accuracy:0.918918918918919 模型测试集Precision:0.88,Recall:0.9777777777777777,F1_score:0.9263157894736842,Accuracy:0.9270833333333334 -------------------测试集混淆举证-------------------
In [11]:
#计算PR曲线和ROC曲线值
Logistic_PR,Logistic_Accuracy=PR_Curve(Model=Logistic,Data=MLRunData,Label=ReMLLabel)
Logistic_ROC,Logistic_AUC=ROC_Curve(Model=Logistic,Data=MLRunData,Label=ReMLLabel)
#输出PR曲线和ROC曲线结果
Logistic_PR.to_excel('/mnt/workspace/Analysis Data/ML PR Data/Logistic_PR.xlsx','UTF-8')
Logistic_ROC.to_excel('/mnt/workspace/Analysis Data/ML ROC Data/Logistic_ROC.xlsx','UTF-8')
/tmp/ipykernel_295/3354021341.py:5: FutureWarning: Starting with pandas version 3.0 all arguments of to_excel except for the argument 'excel_writer' will be keyword-only.
Logistic_PR.to_excel('/mnt/workspace/Analysis Data/ML PR Data/Logistic_PR.xlsx','UTF-8')
/tmp/ipykernel_295/3354021341.py:6: FutureWarning: Starting with pandas version 3.0 all arguments of to_excel except for the argument 'excel_writer' will be keyword-only.
Logistic_ROC.to_excel('/mnt/workspace/Analysis Data/ML ROC Data/Logistic_ROC.xlsx','UTF-8')
In [12]:
#测试SVM算法
from sklearn.svm import SVC
SVM_Classifier=SVC(C=1.0,kernel="rbf",degree=3,gamma="scale",coef0=0.0,shrinking=True,probability=True,tol=1e-3,
cache_size=200,max_iter=-1, decision_function_shape="ovr",random_state=2024)
RunMLModel(Model=SVM_Classifier,Data=MLRunData,Label=ReMLLabel)
模型训练集Precision:0.8934426229508197,Recall:0.956140350877193,F1_Score:0.923728813559322,Accuracy:0.918918918918919 模型测试集Precision:0.9361702127659575,Recall:0.9777777777777777,F1_score:0.9565217391304348,Accuracy:0.9583333333333334 -------------------测试集混淆举证-------------------
In [13]:
#计算PR曲线和ROC曲线值
SVM_PR,SVM_Accuracy=PR_Curve(Model=SVM_Classifier,Data=MLRunData,Label=ReMLLabel)
SVM_ROC,SVM_AUC=ROC_Curve(Model=SVM_Classifier,Data=MLRunData,Label=ReMLLabel)
#输出PR曲线和ROC曲线结果
SVM_PR.to_excel('/mnt/workspace/Analysis Data/ML PR Data/SVM_PR.xlsx','UTF-8')
SVM_ROC.to_excel('/mnt/workspace/Analysis Data/ML ROC Data/SVM_ROC.xlsx','UTF-8')
/tmp/ipykernel_295/4264335228.py:5: FutureWarning: Starting with pandas version 3.0 all arguments of to_excel except for the argument 'excel_writer' will be keyword-only.
SVM_PR.to_excel('/mnt/workspace/Analysis Data/ML PR Data/SVM_PR.xlsx','UTF-8')
/tmp/ipykernel_295/4264335228.py:6: FutureWarning: Starting with pandas version 3.0 all arguments of to_excel except for the argument 'excel_writer' will be keyword-only.
SVM_ROC.to_excel('/mnt/workspace/Analysis Data/ML ROC Data/SVM_ROC.xlsx','UTF-8')
In [ ]:
#测试决策树算法
import numpy as np
from sklearn.model_selection import GridSearchCV
from sklearn.tree import DecisionTreeClassifier
DecisionTree=DecisionTreeClassifier(criterion='gini',splitter='best',max_depth=10,min_samples_split=2,random_state=2024)
Research_params={'max_depth':np.arange(1,20,1,dtype=int),'min_samples_split':np.arange(1,20,1,dtype=int),
'criterion':['gini','entropy']} #超参数列表
GridSearch=GridSearchCV(estimator=DecisionTree,param_grid=Research_params,cv=5,
scoring='accuracy',return_train_score=True,n_jobs=-1,verbose=0)
GridSearch.fit(MLRunData,ReMLLabel)
print('超参数搜索最佳超参数为；',GridSearch.best_params_)
print('超参数搜索最佳得分为:',GridSearch.best_score_)
Best_DTC=GridSearch.best_estimator_
超参数搜索最佳超参数为； {'criterion': 'gini', 'max_depth': 7, 'min_samples_split': 14}
超参数搜索最佳得分为: 0.88015873015873
In [15]:
RunMLModel(Model=Best_DTC,Data=MLRunData,Label=ReMLLabel)
模型训练集Precision:1.0,Recall:0.9649122807017544,F1_Score:0.9821428571428571,Accuracy:0.9819819819819819 模型测试集Precision:0.8913043478260869,Recall:0.9111111111111111,F1_score:0.9010989010989011,Accuracy:0.90625 -------------------测试集混淆举证-------------------
In [ ]:
#计算PR曲线和ROC曲线值
DTC_PR,DTC_Accuracy=PR_Curve(Model=Best_DTC,Data=MLRunData,Label=ReMLLabel)
DTC_ROC,DTC_AUC=ROC_Curve(Model=Best_DTC,Data=MLRunData,Label=ReMLLabel)
#输出PR曲线和ROC曲线结果
DTC_PR.to_excel('/mnt/workspace/Analysis Data/ML PR Data/DTC_PR.xlsx','UTF-8')
DTC_ROC.to_excel('/mnt/workspace/Analysis Data/ML ROC Data/DTC_ROC.xlsx','UTF-8')
In [ ]:
#对随机森林进行网格搜索
from sklearn.ensemble import RandomForestClassifier
RandomForest=RandomForestClassifier(n_estimators=100,criterion='gini',max_depth=7,min_samples_split=6,
bootstrap=True,min_samples_leaf=2,max_features='auto',random_state=2024)
RandomForest_params={'n_estimators':np.arange(1,40,2,dtype=int),'min_samples_leaf':np.arange(1,40,2,dtype=int),
'max_features':['auto','sqrt'],'criterion':['gini','entropy']}
GridSearch_RandomForest=GridSearchCV(estimator=RandomForest,param_grid=RandomForest_params,cv=5,
scoring='accuracy',return_train_score=False,n_jobs=-1,verbose=0)
GridSearch_RandomForest.fit(MLRunData,ReMLLabel)
print('超参数搜索最佳超参数为；',GridSearch_RandomForest.best_params_)
print('超参数搜索最佳得分为:',GridSearch_RandomForest.best_score_)
Best_Forest=GridSearch_RandomForest.best_estimator_
超参数搜索最佳超参数为； {'criterion': 'gini', 'max_features': 'sqrt', 'min_samples_leaf': 3, 'n_estimators': 29}
超参数搜索最佳得分为: 0.9526289682539681
In [18]:
#评估最佳模型
RunMLModel(Model=Best_Forest,Data=MLRunData,Label=ReMLLabel)
模型训练集Precision:0.9912280701754386,Recall:0.9912280701754386,F1_Score:0.9912280701754386,Accuracy:0.990990990990991 模型测试集Precision:0.8913043478260869,Recall:0.9111111111111111,F1_score:0.9010989010989011,Accuracy:0.90625 -------------------测试集混淆举证-------------------
In [ ]:
#计算PR曲线和ROC曲线值
RondomForest_PR,RondomForest_Accuracy=PR_Curve(Model=Best_Forest,Data=MLRunData,Label=ReMLLabel)
RondomForest_ROC,RondomForest_AUC=ROC_Curve(Model=Best_Forest,Data=MLRunData,Label=ReMLLabel)
#输出PR曲线和ROC曲线结果
RondomForest_PR.to_excel('/mnt/workspace/Analysis Data/ML PR Data/RondomForest_PR.xlsx','UTF-8')
RondomForest_ROC.to_excel('/mnt/workspace/Analysis Data/ML ROC Data/RondomForest_ROC.xlsx','UTF-8')
In [20]:
#测试XGBoost模型
from xgboost import XGBClassifier
XGBoost=XGBClassifier(n_jobs=-1,verbosity=1,tree_method='auto',gpu_id=0,random_state=2025)
RunMLModel(Model=XGBoost,Data=MLRunData,Label=ReMLLabel)
/usr/local/lib/python3.11/dist-packages/xgboost/core.py:158: UserWarning: [10:06:40] WARNING: /workspace/src/common/error_msg.cc:45: `gpu_id` is deprecated since2.0.0, use `device` instead. E.g. device=cpu/cuda/cuda:0 warnings.warn(smsg, UserWarning)
模型训练集Precision:1.0,Recall:1.0,F1_Score:1.0,Accuracy:1.0 模型测试集Precision:0.875,Recall:0.9333333333333333,F1_score:0.9032258064516129,Accuracy:0.90625 -------------------测试集混淆举证-------------------
/usr/local/lib/python3.11/dist-packages/xgboost/core.py:158: UserWarning: [10:06:41] WARNING: /workspace/src/common/error_msg.cc:58: Falling back to prediction using DMatrix due to mismatched devices. This might lead to higher memory usage and slower performance. XGBoost is running on: cuda:0, while the input data is on: cpu. Potential solutions: - Use a data structure that matches the device ordinal in the booster. - Set the device for booster before call to inplace_predict. This warning will only be shown once. warnings.warn(smsg, UserWarning)
In [ ]:
#计算PR曲线和ROC曲线值
XGBoost_PR,XGBoost_Accuracy=PR_Curve(Model=XGBoost,Data=MLRunData,Label=ReMLLabel)
XGBoost_ROC,XGBoost_AUC=ROC_Curve(Model=XGBoost,Data=MLRunData,Label=ReMLLabel)
#输出PR曲线和ROC曲线结果
XGBoost_PR.to_excel('/mnt/workspace/Analysis Data/ML PR Data/XGBoost_PR.xlsx','UTF-8')
XGBoost_ROC.to_excel('/mnt/workspace/Analysis Data/ML ROC Data/XGBoost_ROC.xlsx','UTF-8')
In [22]:
#测试LightGBM算法
from lightgbm import LGBMClassifier
LightGBM=LGBMClassifier(boosting_type='gbdt',num_leaves=31,learning_rate=0.01,n_estimators=200,
n_jobs=-1,objective='binary',metric='binary_logloss',keep_training_booster=True,
importance_type='gini',random_state=2025)
RunMLModel(Model=LightGBM,Data=MLRunData,Label=ReMLLabel)
[LightGBM] [Warning] Unknown parameter: keep_training_booster [LightGBM] [Warning] Unknown parameter: keep_training_booster [LightGBM] [Info] Number of positive: 114, number of negative: 108 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001426 seconds. You can set `force_row_wise=true` to remove the overhead. And if memory is not enough, you can set `force_col_wise=true`. [LightGBM] [Info] Total Bins 2829 [LightGBM] [Info] Number of data points in the train set: 222, number of used features: 60 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.513514 -> initscore=0.054067 [LightGBM] [Info] Start training from score 0.054067 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best 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[Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further 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positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] 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positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] Unknown parameter: keep_training_booster 模型训练集Precision:0.9911504424778761,Recall:0.9824561403508771,F1_Score:0.986784140969163,Accuracy:0.9864864864864865 [LightGBM] [Warning] Unknown parameter: keep_training_booster 模型测试集Precision:0.8604651162790697,Recall:0.8222222222222222,F1_score:0.8409090909090909,Accuracy:0.8541666666666666 -------------------测试集混淆举证------------------- [LightGBM] [Warning] Unknown parameter: keep_training_booster
In [ ]:
#计算PR曲线和ROC曲线值
LightGBM_PR,LightGBM_Accuracy=PR_Curve(Model=LightGBM,Data=MLRunData,Label=ReMLLabel)
LightGBM_ROC,LightGBM_AUC=ROC_Curve(Model=LightGBM,Data=MLRunData,Label=ReMLLabel)
#输出PR曲线和ROC曲线结果
LightGBM_PR.to_excel('/mnt/workspace/Analysis Data/ML PR Data/LightGBM_PR.xlsx','UTF-8')
LightGBM_ROC.to_excel('/mnt/workspace/Analysis Data/ML ROC Data/LightGBM_ROC.xlsx','UTF-8')
In [24]:
#测试CatBoost算法
from catboost import CatBoostClassifier
CatBoost=CatBoostClassifier(learning_rate=0.01,depth=6,iterations=500,thread_count=-1)
RunMLModel(Model=CatBoost,Data=MLRunData,Label=ReMLLabel)
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模型训练集Precision:1.0,Recall:1.0,F1_Score:1.0,Accuracy:1.0 模型测试集Precision:0.9111111111111111,Recall:0.9111111111111111,F1_score:0.9111111111111111,Accuracy:0.9166666666666666 -------------------测试集混淆举证-------------------
In [ ]:
#计算PR曲线和ROC曲线值
CatBoost_PR,CatBoost_Accuracy=PR_Curve(Model=CatBoost,Data=MLRunData,Label=ReMLLabel)
CatBoost_ROC,CatBoost_AUC=ROC_Curve(Model=CatBoost,Data=MLRunData,Label=ReMLLabel)
#输出PR曲线和ROC曲线结果
CatBoost_PR.to_excel('/mnt/workspace/Analysis Data/ML PR Data/CatBoost_PR.xlsx','UTF-8')
CatBoost_ROC.to_excel('/mnt/workspace/Analysis Data/ML ROC Data/CatBoost_ROC.xlsx','UTF-8')
In [26]:
#绘制PR曲线
import matplotlib.pyplot as plt
PR_curve=plt.figure(dpi=300)
PR_ax=PR_curve.add_subplot(111)
PR_ax.set_title('Precision-Recall curve')
PR_ax.plot(CatBoost_PR['recall'], CatBoost_PR['precision'],color='red',label='CatBoost=%f'%CatBoost_Accuracy)
PR_ax.plot(DTC_PR['recall'], DTC_PR['precision'],color='green',label='DecisionTree=%f'%DTC_Accuracy)
PR_ax.plot(LightGBM_PR['recall'], LightGBM_PR['precision'],color='blue',label='LightGBM=%f'%LightGBM_Accuracy)
PR_ax.plot(Logistic_PR['recall'], Logistic_PR['precision'],color='yellow',label='Logistic=%f'%Logistic_Accuracy)
PR_ax.plot(RondomForest_PR['recall'], RondomForest_PR['precision'],color='purple',label='RondomForest=%f'%RondomForest_Accuracy)
PR_ax.plot(SVM_PR['recall'], SVM_PR['precision'],color='cyan',label='SVM=%f'%SVM_Accuracy)
PR_ax.plot(XGBoost_PR['recall'], XGBoost_PR['precision'],color='pink',label='XGBoost=%f'%XGBoost_Accuracy)
PR_ax.plot([0,1],[1,0],linestyle='-.',color='black')
PR_ax.set_xlabel('Recall')
PR_ax.set_ylabel('Precision')
plt.legend(loc="best")
plt.show()
In [27]:
#绘制ROC曲线
import matplotlib.pyplot as plt
ROC_curve=plt.figure(dpi=300)
ROC_ax=ROC_curve.add_subplot(111)
ROC_ax.set_title('ROC Curve')
ROC_ax.plot(CatBoost_ROC['fpr'], CatBoost_ROC['tpr'],color='red',label='CatBoost=%f'%CatBoost_AUC)
ROC_ax.plot(DTC_ROC['fpr'], DTC_ROC['tpr'],color='green',label='DecisionTree=%f'%DTC_AUC)
ROC_ax.plot(LightGBM_ROC['fpr'], LightGBM_ROC['tpr'],color='blue',label='LightGBM=%f'%LightGBM_AUC)
ROC_ax.plot(Logistic_ROC['fpr'], Logistic_ROC['tpr'],color='yellow',label='Logistic=%f'%Logistic_AUC)
ROC_ax.plot(RondomForest_ROC['fpr'], RondomForest_ROC['tpr'],color='purple',label='RondomForest=%f'%RondomForest_AUC)
ROC_ax.plot(SVM_ROC['fpr'], SVM_ROC['tpr'],color='cyan',label='SVM=%f'%SVM_AUC)
ROC_ax.plot(XGBoost_ROC['fpr'], XGBoost_ROC['tpr'],color='pink',label='XGBoost=%f'%XGBoost_AUC)
ROC_ax.plot([0,1],[0,1],linestyle='-.',color='black')
ROC_ax.set_xlim([-0.05, 1.0])
ROC_ax.set_ylim([0, 1.05])
ROC_ax.set_xlabel('FPR')
ROC_ax.set_ylabel('TPR')
plt.legend(loc="best")
plt.show()
In [28]:
#保存模型-输入[batchs,61],输出[batch,1]
import joblib
MLModel=[Logistic,SVM_Classifier,Best_DTC,Best_Forest,XGBoost,LightGBM,CatBoost]
MLModelStr=['Logistic.pkl','SVM.pkl','DecisionTree.pkl','Forest.pkl','XGBoost.pkl','LightGBM.pkl','CatBoost.pkl']
try:
for model,modelstr in zip(MLModel,MLModelStr):
joblib.dump(model,'/mnt/workspace/Analysis Model/Meachine Learning Models/'+modelstr)
print('模型保存成功！')
except:
print('模型保存异常！！！')
模型保存成功！
In [29]:
#执行PCA算法-计算保留95%信息时的维度
from sklearn.decomposition import PCA
PCA=PCA(n_components=0.95)
Methylation_95=PCA.fit_transform(MLRunData) #数据降维
Methylation_95.shape
Out[29]:
(318, 22)
In [31]:
#构建SHAP库解析数据集
from sklearn.model_selection import train_test_split
TrainDataSHAP,TestDataSHAP,TrainLabelSHAP,TestLabelSHAP=train_test_split(MLRunData,ReMLLabel,train_size=0.7,random_state=2025)
In [32]:
#载入SHAP库计算SHAP值
import shap
SVMExplainer=shap.KernelExplainer(SVM_Classifier.predict, TestDataSHAP)
shap_values=SVMExplainer.shap_values(TestDataSHAP)
100%|██████████| 96/96 [06:58<00:00, 4.36s/it]
In [33]:
#绘制Summaryplot图
shap.summary_plot(shap_values,TestDataSHAP,max_display=22)
shap.summary_plot(shap_values,TestDataSHAP,max_display=22,plot_type='bar')
In [34]:
#绘制SHAP图
SHAP_ResFormer=(TestDataSHAP)
100%|██████████| 96/96 [07:01<00:00, 4.39s/it]
In [35]:
shap.plots.heatmap(SHAP_SVM,max_display=23)
Out[35]:
<Axes: xlabel='Instances'>
In [36]:
#绘制Decision图
expected_value=SVMExplainer.expected_value
shap.decision_plot(expected_value,shap_values,TestDataSHAP.columns,feature_display_range=slice(None, -23, -1))
In [37]:
#绘制SHAP散点图
shap.plots.scatter(SHAP_SVM[:,'cg00581848'])
shap.plots.scatter(SHAP_SVM[:,'cg00581848'],color=SHAP_SVM)
In [38]:
#绘制SHAP散点图-性别特异性
shap.plots.scatter(SHAP_SVM[:,'GenderEncoder'])
shap.plots.scatter(SHAP_SVM[:,'GenderEncoder'],color=SHAP_SVM)
In [ ]:
#输出SHAP值
SHAPData=DataFrame(shap_values)
SHAPData.to_excel('/mnt/workspace/Analysis Data/SVM_SHAP_Data.xlsx','UFT-8')
SHAPData.to_csv('/mnt/workspace/Analysis Data/SVM_SHAP_Data.csv')
使用LIME（Local Interpretable Model-agnostic Explanations）对SVM模型进行模型解释性。
In [ ]:
#转化为Numpy数组
import numpy as np
TestDataLIME=np.array(TestDataSHAP)
TestDataLIME
In [103]:
#构建LIME解释器
from lime.lime_tabular import LimeTabularExplainer
SVM_LIMEExplainer=LimeTabularExplainer(training_data=TestDataLIME,mode='classification',feature_names=TestDataSHAP.columns,class_names=[0,1], discretize_continuous=True)
In [104]:
#提取类风湿性关节炎患者和健康人数据——为数据生成解释
LIMEData=pd.concat([TestDataSHAP,TestLabelSHAP],axis=1) #合并数据
Contral_Data=LIMEData.loc[LIMEData.loc[:,'DiseaseEncoder']==0,:] #正常组数据
Contral_DataSample=Contral_Data.iloc[0,:-1]
RA_Data=LIMEData.loc[LIMEData.loc[:,'DiseaseEncoder']==1,:] #正常组数据
RA_DataSample=RA_Data.iloc[0,:-1]
In [105]:
#为健康人生成可解释性
Contral_Explain=SVM_LIMEExplainer.explain_instance(Contral_DataSample,SVM_Classifier.predict_proba,num_features=22)
#可视化解释结果
Contral_Explain.show_in_notebook(show_table=True, show_all=False)
Contral_Explain.as_pyplot_figure()
/usr/local/lib/python3.11/dist-packages/lime/discretize.py:110: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]` ret[feature] = int(self.lambdas[feature](ret[feature])) /usr/local/lib/python3.11/dist-packages/lime/discretize.py:110: FutureWarning: Series.__setitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To set a value by position, use `ser.iloc[pos] = value` ret[feature] = int(self.lambdas[feature](ret[feature])) /usr/local/lib/python3.11/dist-packages/lime/lime_tabular.py:544: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]` binary_column = (inverse_column == first_row[column]).astype(int) /usr/local/lib/python3.11/dist-packages/sklearn/base.py:493: UserWarning: X does not have valid feature names, but SVC was fitted with feature names warnings.warn( /usr/local/lib/python3.11/dist-packages/lime/discretize.py:110: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]` ret[feature] = int(self.lambdas[feature](ret[feature])) /usr/local/lib/python3.11/dist-packages/lime/discretize.py:110: FutureWarning: Series.__setitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To set a value by position, use `ser.iloc[pos] = value` ret[feature] = int(self.lambdas[feature](ret[feature])) /usr/local/lib/python3.11/dist-packages/lime/lime_tabular.py:427: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]` discretized_instance[f])]
Out[105]:
In [106]:
#为类风湿性关节炎患者生成可解释性
RA_Explain=SVM_LIMEExplainer.explain_instance(RA_DataSample,SVM_Classifier.predict_proba,num_features=22)
#可视化解释结果
RA_Explain.show_in_notebook(show_table=True, show_all=False)
RA_Explain.as_pyplot_figure()
/usr/local/lib/python3.11/dist-packages/lime/discretize.py:110: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]` ret[feature] = int(self.lambdas[feature](ret[feature])) /usr/local/lib/python3.11/dist-packages/lime/discretize.py:110: FutureWarning: Series.__setitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To set a value by position, use `ser.iloc[pos] = value` ret[feature] = int(self.lambdas[feature](ret[feature])) /usr/local/lib/python3.11/dist-packages/lime/lime_tabular.py:544: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]` binary_column = (inverse_column == first_row[column]).astype(int) /usr/local/lib/python3.11/dist-packages/sklearn/base.py:493: UserWarning: X does not have valid feature names, but SVC was fitted with feature names warnings.warn( /usr/local/lib/python3.11/dist-packages/lime/discretize.py:110: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]` ret[feature] = int(self.lambdas[feature](ret[feature])) /usr/local/lib/python3.11/dist-packages/lime/discretize.py:110: FutureWarning: Series.__setitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To set a value by position, use `ser.iloc[pos] = value` ret[feature] = int(self.lambdas[feature](ret[feature])) /usr/local/lib/python3.11/dist-packages/lime/lime_tabular.py:427: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]` discretized_instance[f])]
Out[106]:
提取SHAP方法和LIME方法提取的特征数据做交集获得候选特征。
In [111]:
#获取交 集数据
SHAP_Feature=['cg00581848','cg05544622','cg00478198','cg01938887','cg17482649','cg00455876','cg11704979','cg01515508',
'cg05257372','cg22221554','cg00423014','cg22561883','cg10315562','cg26012731','cg03601619','cg26039926',
'cg02714462','cg08141049','cg05443523','cg19532714','age','cg07066594'] #SHAP框架提取特征
LIME_Feature=['cg07066594','cg00581848','cg17482649','cg00455876','cg05544622','cg00423014','cg01515508','cg05443523',
'cg08141049','cg26039926','cg00342358','cg23925558','cg16078210','cg00306390','cg00776430','cg01938887',
'cg00543485','cg22561883','cg02896361','cg17488844','cg05257372','cg12944030'] #LIME提取特征
MethylationRA=set(SHAP_Feature)&set(LIME_Feature) #计算交集
MethylationRA=list(MethylationRA)+['age','gender','disease','GenderEncoder','DiseaseEncoder']
MethylationRA
Out[111]:
['cg05544622', 'cg01938887', 'cg07066594', 'cg22561883', 'cg00581848', 'cg05443523', 'cg05257372', 'cg00455876', 'cg17482649', 'cg26039926', 'cg00423014', 'cg01515508', 'cg08141049', 'age', 'gender', 'disease', 'GenderEncoder', 'DiseaseEncoder']
对交集DNA甲基化位点数据进行统计学分析，主要包括：正态检验、差异分析和相关分析。
In [112]:
#获取统计学数据
StatsData=MethylationData.loc[:,MethylationRA]
StatsData.shape
Out[112]:
(318, 18)
In [136]:
#首先进行正态性分析
from scipy.stats import normaltest
CGList=['cg05544622','cg01938887','cg07066594','cg22561883','cg00581848','cg05443523','cg05257372','cg00455876',
'cg17482649','cg26039926','cg00423014','cg01515508','cg08141049','age']
NormalCG,NormalStats,NormalPvalue=[],[],[]
for cg in CGList:
CGData=StatsData.loc[:,cg] #提取数据
normalstats,normalpvalue=normaltest(CGData) #正态检验
NormalCG.append(cg)
NormalStats.append(normalstats)
NormalPvalue.append(normalpvalue)
NormalResult=DataFrame()
NormalResult['Feature']=NormalCG
NormalResult['Normal Stats']=NormalStats
NormalResult['Normal P value']=NormalPvalue
NormalResult=NormalResult.sort_values('Normal P value',ascending=True)
NormalResult
Out[136]:
| Feature | Normal Stats | Normal P value | |
|---|---|---|---|
| 3 | cg22561883 | 45397.441686 | 0.000000e+00 |
| 0 | cg05544622 | 748.223251 | 3.352629e-163 |
| 12 | cg08141049 | 388.753626 | 3.830210e-85 |
| 8 | cg17482649 | 234.045003 | 1.505821e-51 |
| 9 | cg26039926 | 230.466573 | 9.011967e-51 |
| 7 | cg00455876 | 204.595818 | 3.737512e-45 |
| 5 | cg05443523 | 169.865790 | 1.300506e-37 |
| 11 | cg01515508 | 153.367553 | 4.973465e-34 |
| 10 | cg00423014 | 148.633679 | 5.304039e-33 |
| 2 | cg07066594 | 122.553661 | 2.442366e-27 |
| 6 | cg05257372 | 87.312124 | 1.097522e-19 |
| 4 | cg00581848 | 76.637329 | 2.282525e-17 |
| 1 | cg01938887 | 8.991284 | 1.115751e-02 |
| 13 | age | 4.452384 | 1.079387e-01 |
In [137]:
#输出正态检验结果
NormalResult.to_excel('/mnt/workspace/Analysis Data/Stats Results/NormalResult.xlsx','UTF-8')
NormalResult.to_csv('/mnt/workspace/Analysis Data/Stats Results/NormalResult.csv')
In [138]:
#查看数据分布
import seaborn as sns
for cg in CGList:
kde=plt.figure()
sns.distplot(StatsData.loc[:,cg])
plt.show()
In [139]:
#以是否发生类风湿性关节炎分组进行差异分析
#使用Mann-Whitney U和Kruskal-Wallis H进行差异分析
from scipy.stats import mannwhitneyu,kruskal
Diff_name,Mann_W_stats,Mann_W_pvalue=[],[],[]
KW_Stats,KW_Pvalue=[],[]
RA=StatsData.loc[StatsData.loc[:,'disease']=='rheumatoid arthritis',:]
Control=StatsData.loc[StatsData.loc[:,'disease']=='control',:]
for name in CGList:
mann_stats,mann_pvalue=mannwhitneyu(RA.loc[:,name],Control.loc[:,name],alternative='two-sided')
Diff_name.append(name)
Mann_W_stats.append(mann_stats)
Mann_W_pvalue.append(mann_pvalue)
kw_stats,kw_pvalue=kruskal(RA.loc[:,name],Control.loc[:,name])
KW_Stats.append(kw_stats)
KW_Pvalue.append(kw_pvalue)
DiffResult=DataFrame()
DiffResult['CG']=Diff_name
DiffResult['Mann-Whitney U Stats']=Mann_W_stats
DiffResult['Mann-Whitney U Pvalue']=Mann_W_pvalue
DiffResult['Kruskal-Wallis H Stat']=KW_Stats
DiffResult['Kruskal-Wallis H Pvalue']=KW_Pvalue
DiffResult=DiffResult.sort_values('Mann-Whitney U Pvalue',ascending=True)
DiffResult
Out[139]:
| CG | Mann-Whitney U Stats | Mann-Whitney U Pvalue | Kruskal-Wallis H Stat | Kruskal-Wallis H Pvalue | |
|---|---|---|---|---|---|
| 12 | cg08141049 | 18348.5 | 1.423253e-18 | 77.375583 | 1.413519e-18 |
| 9 | cg26039926 | 5909.5 | 2.189873e-16 | 67.433837 | 2.178774e-16 |
| 6 | cg05257372 | 6207.5 | 3.556541e-15 | 61.940912 | 3.539194e-15 |
| 7 | cg00455876 | 8289.5 | 2.437285e-14 | 58.156458 | 2.420782e-14 |
| 4 | cg00581848 | 16820.0 | 6.371911e-13 | 51.741242 | 6.331863e-13 |
| 8 | cg17482649 | 9202.0 | 1.038787e-09 | 37.261518 | 1.033031e-09 |
| 10 | cg00423014 | 7932.5 | 2.694328e-09 | 35.400669 | 2.683947e-09 |
| 11 | cg01515508 | 17054.5 | 7.118968e-08 | 29.038656 | 7.094835e-08 |
| 5 | cg05443523 | 15647.0 | 4.901541e-07 | 25.310599 | 4.880197e-07 |
| 2 | cg07066594 | 16273.5 | 3.926378e-06 | 21.306322 | 3.914385e-06 |
| 0 | cg05544622 | 9963.0 | 1.647483e-04 | 14.200984 | 1.642846e-04 |
| 1 | cg01938887 | 11455.0 | 1.482944e-01 | 2.091472 | 1.481233e-01 |
| 3 | cg22561883 | 11780.5 | 2.944031e-01 | 1.100655 | 2.941223e-01 |
| 13 | age | 13486.5 | 3.022940e-01 | 1.065306 | 3.020081e-01 |
In [140]:
#输出差异分析结果
DiffResult.to_excel('/mnt/workspace/Analysis Data/Stats Results/DiffResult.xlsx','UTF-8')
DiffResult.to_csv('/mnt/workspace/Analysis Data/Stats Results/DiffResult.csv')
In [135]:
StatsData
Out[135]:
| cg05544622 | cg01938887 | cg07066594 | cg22561883 | cg00581848 | cg05443523 | cg05257372 | cg00455876 | cg17482649 | cg26039926 | cg00423014 | cg01515508 | cg08141049 | age | gender | disease | GenderEncoder | DiseaseEncoder | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.000000 | -1.271175 | -1.348848 | -0.176421 | -2.616463 | -2.441207 | 3.938986 | 0.000000 | 0.000000 | -2.749976 | 1.202370 | 3.701353 | -0.866223 | 19.0000 | F | control | 1.0 | 0 |
| 1 | 0.000000 | -1.130596 | -1.495729 | 0.347383 | -2.468664 | -1.536363 | 3.938986 | 0.000000 | 0.900040 | -3.052934 | 1.019899 | 3.543689 | -1.265340 | 12.0000 | F | control | 1.0 | 0 |
| 2 | 0.000000 | -1.168907 | -0.553611 | -0.196592 | 0.000000 | -1.071863 | 3.580953 | 0.000000 | 0.856647 | -3.228904 | 0.241113 | 1.557086 | -0.575243 | 71.8192 | F | control | 1.0 | 0 |
| 3 | 4.178048 | -2.921730 | -3.201956 | -3.701353 | -2.732410 | 0.000000 | 3.472874 | 0.510719 | 0.000000 | -2.664729 | 1.839820 | 4.178048 | 0.000000 | 23.0000 | M | control | 0.0 | 0 |
| 4 | 0.000000 | -1.456115 | -2.131436 | 0.011998 | 0.000000 | 0.000000 | 3.938986 | 0.000000 | 0.372163 | -2.131436 | 0.837604 | 3.938986 | -1.635580 | 84.0000 | F | control | 1.0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 313 | 0.000000 | -1.798190 | -1.236447 | -0.019997 | 0.000000 | 0.000000 | 3.580953 | 0.000000 | 0.000000 | -2.963760 | 0.000000 | 4.402578 | 0.000000 | 34.0000 | F | rheumatoid arthritis | 1.0 | 1 |
| 314 | 3.659672 | -4.112908 | 0.000000 | -4.247583 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | -2.253116 | 0.000000 | 3.993781 | 0.000000 | 51.0000 | M | rheumatoid arthritis | 0.0 | 1 |
| 315 | 0.000000 | -2.163468 | 0.000000 | -0.180452 | 0.000000 | 0.000000 | 3.343970 | 0.000000 | 0.000000 | -3.343970 | 1.130596 | 3.472874 | 0.000000 | 45.0000 | F | rheumatoid arthritis | 1.0 | 1 |
| 316 | 0.000000 | -1.578093 | 0.000000 | -0.088039 | 0.000000 | 0.000000 | 3.256540 | 0.000000 | 0.000000 | -2.767818 | 0.000000 | 4.178048 | 0.000000 | 31.0000 | F | rheumatoid arthritis | 1.0 | 1 |
| 317 | 0.000000 | -1.141464 | -0.895180 | -0.224898 | 0.000000 | 0.000000 | 3.149987 | 0.000000 | 0.000000 | -3.076403 | 1.672671 | 4.940737 | 0.000000 | 58.0000 | F | rheumatoid arthritis | 1.0 | 1 |
318 rows × 18 columns
In [152]:
#绘制特异性数据箱型图
for names in CGList:
plt.figure(dpi=300)
sns.boxplot(StatsData,x='disease',y=names,showmeans=True)
plt.show()
In [141]:
#以是类风湿性关节炎性别分组进行差异分析
#使用Mann-Whitney U和Kruskal-Wallis H进行差异分析
from scipy.stats import mannwhitneyu,kruskal
SexDiff_name,SexMann_W_stats,SexMann_W_pvalue=[],[],[]
SexKW_Stats,SexKW_Pvalue=[],[]
Man=RA.loc[RA.loc[:,'gender']=='M',:]
Feman=RA.loc[RA.loc[:,'gender']=='F',:]
for name in CGList:
mann_stats,mann_pvalue=mannwhitneyu(Man.loc[:,name],Feman.loc[:,name],alternative='two-sided')
SexDiff_name.append(name)
SexMann_W_stats.append(mann_stats)
SexMann_W_pvalue.append(mann_pvalue)
kw_stats,kw_pvalue=kruskal(Man.loc[:,name],Feman.loc[:,name])
SexKW_Stats.append(kw_stats)
SexKW_Pvalue.append(kw_pvalue)
SexDiffResult=DataFrame()
SexDiffResult['CG']=SexDiff_name
SexDiffResult['Mann-Whitney U Stats']=SexMann_W_stats
SexDiffResult['Mann-Whitney U Pvalue']=SexMann_W_pvalue
SexDiffResult['Kruskal-Wallis H Stat']=SexKW_Stats
SexDiffResult['Kruskal-Wallis H Pvalue']=SexKW_Pvalue
SexDiffResult=DiffResult.sort_values('Mann-Whitney U Pvalue',ascending=True)
SexDiffResult
Out[141]:
| CG | Mann-Whitney U Stats | Mann-Whitney U Pvalue | Kruskal-Wallis H Stat | Kruskal-Wallis H Pvalue | |
|---|---|---|---|---|---|
| 12 | cg08141049 | 18348.5 | 1.423253e-18 | 77.375583 | 1.413519e-18 |
| 9 | cg26039926 | 5909.5 | 2.189873e-16 | 67.433837 | 2.178774e-16 |
| 6 | cg05257372 | 6207.5 | 3.556541e-15 | 61.940912 | 3.539194e-15 |
| 7 | cg00455876 | 8289.5 | 2.437285e-14 | 58.156458 | 2.420782e-14 |
| 4 | cg00581848 | 16820.0 | 6.371911e-13 | 51.741242 | 6.331863e-13 |
| 8 | cg17482649 | 9202.0 | 1.038787e-09 | 37.261518 | 1.033031e-09 |
| 10 | cg00423014 | 7932.5 | 2.694328e-09 | 35.400669 | 2.683947e-09 |
| 11 | cg01515508 | 17054.5 | 7.118968e-08 | 29.038656 | 7.094835e-08 |
| 5 | cg05443523 | 15647.0 | 4.901541e-07 | 25.310599 | 4.880197e-07 |
| 2 | cg07066594 | 16273.5 | 3.926378e-06 | 21.306322 | 3.914385e-06 |
| 0 | cg05544622 | 9963.0 | 1.647483e-04 | 14.200984 | 1.642846e-04 |
| 1 | cg01938887 | 11455.0 | 1.482944e-01 | 2.091472 | 1.481233e-01 |
| 3 | cg22561883 | 11780.5 | 2.944031e-01 | 1.100655 | 2.941223e-01 |
| 13 | age | 13486.5 | 3.022940e-01 | 1.065306 | 3.020081e-01 |
In [142]:
#输出差异分析结果
SexDiffResult.to_excel('/mnt/workspace/Analysis Data/Stats Results/SexDiffResult.xlsx','UTF-8')
SexDiffResult.to_csv('/mnt/workspace/Analysis Data/Stats Results/SexDiffResult.csv')
In [153]:
#绘制特异性数据箱型图
for names in CGList:
plt.figure(dpi=300)
sns.boxplot(StatsData,x='gender',y=names,showmeans=True)
plt.show()
In [143]:
#绘制箱型图
for cg_name in CGList:
box=plt.figure(dpi=300)
sns.boxplot(StatsData,x='disease',y=cg_name,hue='gender')
plt.show()
计算相关性：年龄及其性别数据(cg07066594)的相关性。
In [156]:
#计算相关性
Spearman=StatsData.loc[:,CGList].corr(method='spearman')
plt.figure(dpi=300)
sns.clustermap(Spearman,annot=True)
plt.show()
<Figure size 1920x1440 with 0 Axes>
In [163]:
#绘制散点图查看数据分布
Scatter=plt.figure(dpi=300)
StatterAx=Scatter.add_subplot()
StatterAx.set_title('Scatterplot of methylation data')
scatter_plot=StatterAx.scatter(x=StatsData.loc[:,'cg00581848'],y=StatsData.loc[:,'cg05544622'],c=StatsData.loc[:,'cg07066594'],cmap='jet')
StatterAx.set_xlabel('cg00581848')
StatterAx.set_ylabel('cg05544622')
Scatter.colorbar(scatter_plot)
plt.show()
In [164]:
#绘制散点图查看数据分布
Scatter=plt.figure(dpi=300)
StatterAx=Scatter.add_subplot()
StatterAx.set_title('Scatterplot of methylation data')
scatter_plot=StatterAx.scatter(x=StatsData.loc[:,'cg00581848'],y=StatsData.loc[:,'cg05544622'],c=StatsData.loc[:,'age'],cmap='jet')
StatterAx.set_xlabel('cg00581848')
StatterAx.set_ylabel('cg05544622')
Scatter.colorbar(scatter_plot)
plt.show()
In [165]:
#输出统计学结果
StatsData.to_excel('/mnt/workspace/DNA methylation data/RA DNA methylation/StatsRawData.xlsx','UTF-8')
StatsData.to_csv('/mnt/workspace/DNA methylation data/RA DNA methylation/StatsRawData.csv')
In [ ]: